In many modern automobiles, a driver may control speed manually by depressing and releasing the automobile's gas pedal or automatically by operating a speed or cruise control device that electronically maintains the automobile's speed. When using the known types of automatic cruise control features, a driver selects the speed at which to drive the car and sets the cruise control. Thereafter, unless the operator interrupts the cruise control device, the automobile will continue to travel at the set speed irrespective of proximity to other automobiles in its path. Thus, known cruise control devices cannot adjust an automobile's speed in response to the rapid approach to another automobile.
One attempt to address this problem is called adaptive cruise control (ACC). With ACC, the cruise control function of the automobile adapts to miscalculations on the part of the driver as evidenced by the rapid approach to another vehicle. An essential element in establishing ACC function is the ability to determine range and range-rate between the ACC-equipped automobile and other vehicles.
Passive ranging is one method of determining the range between a sensor and a moving object. A number of approaches have been suggested in computer vision literature to address the problem of passive ranging. These approaches can be broadly classified into motion analysis, stereo methods, and other approaches. Motion analysis can be further subdivided into optical flow approaches and structure and motion methods.
Optical flow uses the displacement field produced due to apparent motion of objects through an image sequence. Optical flow computation can be achieved by gradient-based or displacement-based methods. Gradient-based methods require the computation of spatio-temporal image intensity gradients which can be quite sensitive to noise. In fact, these techniques have often been criticized in literature as not being useful in real-life situations due to their noise sensitivity. Displacement-based methods tend to be more computationally expensive than the gradient-based methods as they require the extraction of features such as points, edges, or regions. These features are then matched across images to compute flow field estimates. Feature correspondence is a computationally expensive task. Without either improvement against noise sensitivity in gradient-based methods or a method to reduce the computational expense in displacement-based methods, optical flow methods have limited utility for motion analysis in adaptive cruise control.
Structure and motion methods, while being theoretically elegant, are sensitive to noise in practice and require large amounts of computation, converge slowly, and require many disparate views of the object. As a result, structure and motion methods have limited utility in passive ranging for adaptive cruise control applications. Likewise, stereo techniques, which rely on inputs from two or more sensing devices, have limited adaptive cruise control applicability because of the need for correspondence between images obtained from two cameras. The correspondence accuracy depends on knowledge from the relative camera positions, displacement between cameras, and the availability of prominent features on the target objects to match. Thus, stereo techniques do not offer acceptable passive ranging methods for adaptive cruise control.
There is the need for a method and system to determine target range and range rate for adaptive cruise control purposes.
There is also the need for a passive ranging method and system that satisfies requirements of automobile manufacturers for adaptive cruise control applications.
There is a further need for a passive ranging method and system that is relatively insensitive to noise, computationally inexpensive, and that does not require the use of multiple sensing devices for stereo signals from which to compute range.
The approach of S. Boucher, J. M. Blosseville, and F. Lenoir, "Traffic Spatial Measurements Using Video Image Processing (Application of Mathematical Morphology to Vehicles Detection)" SPIE Vol. 848 Intelligent Robots and Computer Vision 1987, ("Boucher") illustrates the use of an image processing system for traffic scene analysis. The system that Boucher describes performs the tasks of lane detection, vehicle detection, and vehicle tracking. While the Boucher system and method uses image processing for many applications, it is a sophisticated and expensive approach. The computational requirements and expense of the Boucher system, therefore, place it far outside the cost realm of the adaptive cruise control function for mass-produced automobiles.
Thus, there is a need for a passive ranging method and system that satisfies the cost limitations of automobile manufacturers. There is the need for a passive ranging method and system for use in adaptive cruise control that is both reliable and safe for use in consumer automobiles.
There is yet a need for a passive ranging system for use in adaptive cruise control systems that satisfies all of the above needs both efficiently and effectively.